SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

Showing 35013550 of 6433 papers

TitleStatusHype
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation0
Training Recurrent Neural Networks against Noisy Computations during Inference0
Training Speech Enhancement Systems with Noisy Speech Datasets0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework0
Training variance and performance evaluation of neural networks in speech0
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers0
Trait-Based Hypothesis Selection For Machine Translation0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications0
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation0
Transcribe, Align and Segment: Creating speech datasets for low-resource languages0
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR0
Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition0
Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos0
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition0
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation0
Trans-dimensional Random Fields for Language Modeling0
Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition0
Transducers with Pronunciation-aware Embeddings for Automatic Speech Recognition0
Transferable Adversarial Attacks against ASR0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification0
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System0
Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments0
Transfer Learning for Algorithm Recommendation0
Transfer Learning for British Sign Language Modelling0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping0
Transfer Learning for Speech and Language Processing0
Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations0
Transfer Learning from Audio-Visual Grounding to Speech Recognition0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization0
Transfer Learning from Whisper for Microscopic Intelligibility Prediction0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak0
Transferring Knowledge from a RNN to a DNN0
Transformer ASR with Contextual Block Processing0
Transformer-based Acoustic Modeling for Hybrid Speech Recognition0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation0
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project0
Transformer Based Deliberation for Two-Pass Speech Recognition0
Transformer-based end-to-end speech recognition with residual Gaussian-based self-attention0
Transformer-based language modeling and decoding for conversational speech recognition0
Transformer-based Model for ASR N-Best Rescoring and Rewriting0
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture0
Transformer-based Streaming ASR with Cumulative Attention0
Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video0
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications0
Transformers in Speech Processing: A Survey0
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention0
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified